Evaluating the relationship between magnetic resonance image quality metrics and deep learning-based segmentation accuracy of brain tumors.

BACKGROUND: Magnetic resonance imaging (MRI) scans are known to suffer from a variety of acquisition artifacts as well as equipment-based variations that impact image appearance and segmentation performance. It is still unclear whether a direct relationship exists between magnetic resonance (MR) image quality metrics (IQMs) (e.g., signal-to-noise, contrast-to-noise) and segmentation accuracy. PURPOSE: Deep learning (DL) approaches have shown significant promise for automated segmentation of brain tumors on MRI but depend on the quality of input training images. We sought to evaluate the relationship between IQMs of input training images and DL-based brain tumor segmentation accuracy toward developing more generalizable models for multi-institutional data. METHODS: We trained a 3D DenseNet model on the BraTS 2020 cohorts for segmentation of tumor subregions enhancing tumor (ET), peritumoral edematous, and necrotic and non-ET on MRI; with performance quantified via a 5-fold cross-validated Dice coefficient. MRI scans were evaluated through the open-source quality control tool MRQy, to yield 13 IQMs per scan. The Pearson correlation coefficient was computed between whole tumor (WT) dice values and IQM measures in the training cohorts to identify quality measures most correlated with segmentation performance. Each selected IQM was used to group MRI scans as "better" quality (BQ) or "worse" quality (WQ), via relative thresholding. Segmentation performance was re-evaluated for the DenseNet model when (i) training on BQ MRI images with validation on WQ images, as well as (ii) training on WQ images, and validation on BQ images. Trends were further validated on independent test sets derived from the BraTS 2021 training cohorts. RESULTS: For this study, multimodal MRI scans from the BraTS 2020 training cohorts were used to train the segmentation model and validated on independent test sets derived from the BraTS 2021 cohort. Among the selected IQMs, models trained on BQ images based on inhomogeneity measurements (coefficient of variance, coefficient of joint variation, coefficient of variation of the foreground patch) and the models trained on WQ images based on noise measurement peak signal-to-noise ratio (SNR) yielded significantly improved tumor segmentation accuracy compared to their inverse models. CONCLUSIONS: Our results suggest that a significant correlation may exist between specific MR IQMs and DenseNet-based brain tumor segmentation performance. The selection of MRI scans for model training based on IQMs may yield more accurate and generalizable models in unseen validation.

A novel method to guide biomarker combinations to optimize the sensitivity.

Logistic regression has demonstrated its utility in classifying binary labeled datasets through the maximum likelihood approach. However, in numerous biological and clinical contexts, the aim is often to determine coefficients that yield the highest sensitivity at the pre-specified specificity or vice versa. Therefore, the application of logistic regression is limited in such settings. To this end, we have developed an improved regression framework, SMAGS, for binary classification that, for a given specificity, finds the linear decision rule that yields the maximum sensitivity. Furthermore, we employed the method for feature selection to find the features that are satisfying the sensitivity maximization goal. We compared our method with normal logistic regression by applying it to real clinical data as well as synthetic data. In the real application data (colorectal cancer dataset), we found 14% improvement of sensitivity at 98.5% specificity. Availability and implementation: Software is made available in Python ( https://github.com/smahmoodghasemi/SMAGS ).

Cabozantinib monotherapy for advanced adrenocortical carcinoma: a single-arm, phase 2 trial.

BACKGROUND: Adrenocortical carcinoma is a rare malignancy with poor response to systemic chemotherapy. Mitotane is the only approved therapy for adrenocortical carcinoma. Cabozantinib is a multikinase inhibitor approved in multiple malignancies. This is the first prospective trial to explore the anti-tumour activity, safety, and pharmacokinetic profile of cabozantinib in patients with advanced adrenocortical carcinoma. METHODS: This investigator-initiated, single-arm, phase 2 trial in adult patients (aged ≥18 years) with advanced adrenocortical carcinoma was done at the University of Texas MD Anderson Cancer Center (Houston, TX, USA). Eligible patients had histologically confirmed adrenocortical carcinoma, were not candidates for surgery with curative intent, had measurable disease, had an estimated life expectancy of at least 3 months, and an Eastern Cooperative Oncology Group (ECOG) performance status of 0-2 with adequate organ function. Patients who had used mitotane within 6 months of study participation were required to have a serum mitotane level of less than 2 mg/L. Patients were given oral cabozantinib 60 mg daily with the option of dose reduction to manage adverse events. The primary endpoint was progression-free survival at 4 months, assessed in all patients who received at least one dose of study drug per protocol. This study is registered with ClinicalTrials.gov, NCT03370718, and is now complete. FINDINGS: Between March 1, 2018, and May 31, 2021, we enrolled 18 patients (ten males and eight females), all of whom received at least one dose of study treatment. Of the 18 patients, eight (44%) had an ECOG performance status of 0, nine (50%) patients had a performance status of 1, and one (6%) patient had a performance status of 2. Median follow-up was 36·8 months (IQR 30·2-50·3). At 4 months, 13 (72·2%; 95% CI 46·5-90·3) of 18 patients had progression-free survival and median progression-free survival was 6 months (95% CI 4·3 to not reached). One patient remains on treatment. Treatment-related adverse events of grade 3 or worse occurred in 11 (61%) of 18 patients. The most common grade 3 adverse events were lipase elevation (three [17%] of 18 patients), elevated γ-glutamyl transferase concentrations (two [11%] patients), elevated alanine aminotransferase concentrations (two [11%] patients), hypophosphatemia (two [11%] patients), and hypertension (two [11%] patients). One (6%) of 18 patients had grade 4 hypertension. No treatment related deaths occurred on study. INTERPRETATION: Cabozantinib in advanced adrenocortical carcinoma showed promising efficacy with a manageable and anticipated safety profile. Further prospective studies with cabozantinib alone and in combination with immune checkpoint therapy are ongoing. FUNDING: Exelixis.

Modulating a prebiotic food source influences inflammation and immune-regulating gut microbes and metabolites: insights from the BE GONE trial.

BACKGROUND: Accessible prebiotic foods hold strong potential to jointly target gut health and metabolic health in high-risk patients. The BE GONE trial targeted the gut microbiota of obese surveillance patients with a history of colorectal neoplasia through a straightforward bean intervention. METHODS: This low-risk, non-invasive dietary intervention trial was conducted at MD Anderson Cancer Center (Houston, TX, USA). Following a 4-week equilibration, patients were randomized to continue their usual diet without beans (control) or to add a daily cup of study beans to their usual diet (intervention) with immediate crossover at 8-weeks. Stool and fasting blood were collected every 4 weeks to assess the primary outcome of intra and inter-individual changes in the gut microbiome and in circulating markers and metabolites within 8 weeks. This study was registered on ClinicalTrials.gov as NCT02843425, recruitment is complete and long-term follow-up continues. FINDINGS: Of the 55 patients randomized by intervention sequence, 87% completed the 16-week trial, demonstrating an increase on-intervention in diversity [n = 48; linear mixed effect and 95% CI for inverse Simpson index: 0.16 (0.02, 0.30); p = 0.02] and shifts in multiple bacteria indicative of prebiotic efficacy, including increased Faecalibacterium, Eubacterium and Bifidobacterium (all p < 0.05). The circulating metabolome showed parallel shifts in nutrient and microbiome-derived metabolites, including increased pipecolic acid and decreased indole (all p < 0.002) that regressed upon returning to the usual diet. No significant changes were observed in circulating lipoproteins within 8 weeks; however, proteomic biomarkers of intestinal and systemic inflammatory response, fibroblast-growth factor-19 increased, and interleukin-10 receptor-α decreased (p = 0.01). INTERPRETATION: These findings underscore the prebiotic and potential therapeutic role of beans to enhance the gut microbiome and to regulate host markers associated with metabolic obesity and colorectal cancer, while further emphasizing the need for consistent and sustainable dietary adjustments in high-risk patients. FUNDING: This study was funded by the American Cancer Society.

A blood-based metabolomic signature predictive of risk for pancreatic cancer.

Emerging evidence implicates microbiome involvement in the development of pancreatic cancer (PaCa). Here, we investigate whether increases in circulating microbial-related metabolites associate with PaCa risk by applying metabolomics profiling to 172 sera collected within 5 years prior to PaCa diagnosis and 863 matched non-subject sera from participants in the Prostate, Lung, Colorectal, and Ovarian (PLCO) cohort. We develop a three-marker microbial-related metabolite panel to assess 5-year risk of PaCa. The addition of five non-microbial metabolites further improves 5-year risk prediction of PaCa. The combined metabolite panel complements CA19-9, and individuals with a combined metabolite panel + CA19-9 score in the top 2.5th percentile have absolute 5-year risk estimates of >13%. The risk prediction model based on circulating microbial and non-microbial metabolites provides a potential tool to identify individuals at high risk of PaCa that would benefit from surveillance and/or from potential cancer interception strategies.

Detection method has independent prognostic significance in the PLCO lung screening trial.

Prognostic models in cancer use patient demographic and tumor characteristics to predict survival and dynamic disease prognosis. Past work in breast cancer has shown that cancer detection method, screen-detected or symptom-detected, has prognostic significance. We investigate this phenomenon in the lung component of the Prostate, Lung, Colorectal, and Ovarian (PLCO) screening trial. Patients were randomized to intervention, receiving four annual chest x-rays (CXRs), or to control, receiving usual care. Patients were followed for a total of approximately 13 years. In PLCO, lung cancer detection method has independent prognostic value exceeding that of variables commonly used in lung cancer prognostic models, including sex, histology, and age. Results are robust to cohort selection and type of predictive model. These results imply that detection method should be considered when developing prognostic models in lung cancer studies, and cancer registries should routinely collect cancer detection method.

Postoperative Management of Recurrence After Radiosurgery and Surgical Resection for Brain Metastases and Predicting Benefit From Adjuvant Radiation.

Stereotactic radiosurgery (SRS) is often used as upfront treatment for brain metastases. Progression or radionecrosis after SRS is common and can prompt resection. However, postoperative management strategies after resection for SRS failure vary widely, and no standard practice has been established. In this approved study, we retrospectively reviewed patients who received SRS for a brain metastasis followed by resection of the same lesion. We extracted patient-, disease-, and treatment-related variables and information on disease-related outcomes. Univariate and multivariate analyses of clinicopathologic variables were used to create a model to predict factors associated with local failure (LF). A total of 225 patients with brain metastases treated with SRS from 2009 to 2017 followed by surgical resection were identified. Overall, 65% of cases had gross total resection (GTR) on postoperative imaging review. Twenty-one patients (9.3%) received adjuvant radiation therapy to the surgical cavity, and 204 (90.7%) were observed. Of these 204 patients, 118 had GTR with evidence of tumor within the pathology specimen. With a median follow-up of 13 months after resection, 47 patients (40%) developed LF after surgery. After salvage resection of a brain metastasis initially treated with SRS, the observed LF rate was 40% among those who had a GTR and evidence of tumor on pathologic examination. This LF rate is sufficiently high that adjuvant radiation to the surgical bed after salvage resection should be considered in these cases when there is tumor in the pathology, even after a GTR.

Mortality Benefit of a Blood-Based Biomarker Panel for Lung Cancer on the Basis of the Prostate, Lung, Colorectal, and Ovarian Cohort.

PURPOSE: To investigate the utility of integrating a panel of circulating protein biomarkers in combination with a risk model on the basis of subject characteristics to identify individuals at high risk of harboring a lethal lung cancer. METHODS: Data from an established logistic regression model that combines four-marker protein panel (4MP) together with the Prostate, Lung, Colorectal, and Ovarian (PLCO) risk model (PLCOm2012) assayed in prediagnostic sera from 552 lung cancer cases and 2,193 noncases from the PLCO cohort were used in this study. Of the 552 lung cancer cases, 387 (70%) died of lung cancer. Cumulative incidence of lung cancer death and subdistributional and cause-specific hazard ratios (HRs) were calculated on the basis of 4MP + PLCOm2012 risk scores at a predefined 1.0% and 1.7% 6-year risk thresholds, which correspond to the current and former US Preventive Services Task Force screening criteria, respectively. RESULTS: When considering cases diagnosed within 1 year of blood draw and all noncases, the area under receiver operation characteristics curve estimate of the 4MP + PLCOm2012 model for risk prediction of lung cancer death was 0.88 (95% CI, 0.86 to 0.90). The cumulative incidence of lung cancer death was statistically significantly higher in individuals with 4MP + PLCOm2012 scores above the 1.0% 6-year risk threshold (modified χ2, 166.27; P < .0001). Corresponding subdistributional and lung cancer death-specific HRs for test-positive cases were 9.88 (95% CI, 6.44 to 15.18) and 10.65 (95% CI, 6.93 to 16.37), respectively. CONCLUSION: The blood-based biomarker panel in combination with PLCOm2012 identifies individuals at high risk of a lethal lung cancer.

A unified mediation analysis framework for integrative cancer proteogenomics with clinical outcomes.

MOTIVATION: Multilevel molecular profiling of tumors and the integrative analysis with clinical outcomes have enabled a deeper characterization of cancer treatment. Mediation analysis has emerged as a promising statistical tool to identify and quantify the intermediate mechanisms by which a gene affects an outcome. However, existing methods lack a unified approach to handle various types of outcome variables, making them unsuitable for high-throughput molecular profiling data with highly interconnected variables. RESULTS: We develop a general mediation analysis framework for proteogenomic data that include multiple exposures, multivariate mediators on various scales of effects as appropriate for continuous, binary and survival outcomes. Our estimation method avoids imposing constraints on model parameters such as the rare disease assumption, while accommodating multiple exposures and high-dimensional mediators. We compare our approach to other methods in extensive simulation studies at a range of sample sizes, disease prevalence and number of false mediators. Using kidney renal clear cell carcinoma proteogenomic data, we identify genes that are mediated by proteins and the underlying mechanisms on various survival outcomes that capture short- and long-term disease-specific clinical characteristics. AVAILABILITY AND IMPLEMENTATION: Software is made available in an R package (https://github.com/longjp/mediateR). SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Tertiary lymphoid structure signatures are associated with immune checkpoint inhibitor related acute interstitial nephritis.

Tertiary lymphoid structures (TLSs) are associated with anti-tumor response following immune checkpoint inhibitor (ICI) therapy, but a commensurate observation of TLS is absent for immune related adverse events (irAEs) i.e. acute interstitial nephritis (AIN). We hypothesized that TLS-associated inflammatory gene signatures are present in AIN and performed NanoString-based gene expression and multiplex 12-chemokine profiling on paired kidney tissue, urine and plasma specimens of 36 participants who developed acute kidney injury (AKI) on ICI therapy: AIN (18), acute tubular necrosis (9), or HTN nephrosclerosis (9). Increased T and B cell scores, a Th1-CD8+ T cell axis accompanied by interferon-g and TNF superfamily signatures were detected in the ICI-AIN group. TLS signatures were significantly increased in AIN cases and supported by histopathological identification. Furthermore, urinary TLS signature scores correlated with ICI-AIN diagnosis but not paired plasma. Urinary CXCL9 correlated best to tissue CXCL9 expression (rho 0.75, p < 0.001) and the ability to discriminate AIN vs. non-AIN (AUC 0.781, p-value 0.003). For the first time, we report the presence of TLS signatures in irAEs, define distinctive immune signatures, identify chemokine markers distinguishing ICI-AIN from common AKI etiologies and demonstrate that urine chemokine markers may be used as a surrogate for ICI-AIN diagnoses.

Natural History of Marburg Virus Infection to Support Medical Countermeasure Development.

The Biomedical Advanced Research and Development Authority, part of the Administration for Strategic Preparedness and Response within the U.S. Department of Health and Human Services, recognizes that the evaluation of medical countermeasures under the Animal Rule requires well-characterized and reproducible animal models that are likely to be predictive of clinical benefit. Marburg virus (MARV), one of two members of the genus Marburgvirus, is characterized by a hemorrhagic fever and a high case fatality rate for which there are no licensed vaccines or therapeutics available. This natural history study consisted of twelve cynomolgus macaques challenged with 1000 PFU of MARV Angola and observed for body weight, temperature, viremia, hematology, clinical chemistry, and coagulation at multiple time points. All animals succumbed to disease within 8 days and exhibited signs consistent with those observed in human cases, including viremia, fever, systemic inflammation, coagulopathy, and lymphocytolysis, among others. Additionally, this study determined the time from exposure to onset of disease manifestations and the time course, frequency, and magnitude of the manifestations. This study will be instrumental in the design and development of medical countermeasures to Marburg virus disease.

Urinary T cells are detected in patients with immune checkpoint inhibitor-associated immune nephritis that are clonotypically identical to kidney T cell infiltrates.

Acute kidney injury (AKI) occurs in ~20% of patients receiving immune checkpoint inhibitor (ICI) therapy; however, only 2-5% will develop ICI-mediated immune nephritis. Conventional tests are nonspecific in diagnosing disease pathology and invasive procedures (i.e. kidney biopsy) may not be feasible. In other autoimmune renal diseases, urinary immune cells correlated with the pathology or were predictive of disease activity. Corresponding evidence and analysis are absent for ICI-mediated immune nephritis. We report the first investigation analyzing immune cell profiles of matched kidney biopsies and urine of patients with ICI-AKI. We demonstrated the presence of urinary T cells in patients with immune nephritis by flow cytometry analysis. Clonotype analysis of T cell receptor (TCR) sequences confirmed enrichment of kidney TCRs in urine. As ICI therapies become standard of care for more cancers, noninvasively assessing urinary immune cells of ICI therapy recipients can facilitate clinical management and an opportunity to tailor ICI-nephritis treatment.

A Comprehensive Search of Non-Canonical Proteins in Non-Small Cell Lung Cancer and Their Impact on the Immune Response.

There is substantial interest in mining neoantigens for cancer applications. Non-canonical proteins resulting from frameshift mutations have been identified as neoantigens in cancer. We investigated the landscape of non-canonical proteins in non-small cell lung cancer (NSCLC) and their induced immune response in the form of autoantibodies. A database of cryptoproteins was computationally constructed and comprised all alternate open reading frames (altORFs) and ORFs identified in pseudogenes, noncoding RNAs, and untranslated regions of mRNAs that did not align with known canonical proteins. Proteomic profiles of seventeen lung adenocarcinoma (LUAD) cell lines were searched to evaluate the occurrence of cryptoproteins. To assess the immunogenicity, immunoglobulin (Ig)-bound cryptoproteins in plasmas were profiled by mass spectrometry. The specimen set consisted of plasmas from 30 newly diagnosed NSCLC cases, pre-diagnostic plasmas from 51 NSCLC cases, and 102 control plasmas. An analysis of LUAD cell lines identified 420 cryptoproteins. Plasma Ig-bound analyses revealed 90 cryptoproteins uniquely found in cases and 14 cryptoproteins that had a fold-change >2 compared to controls. In pre-diagnostic samples, 17 Ig-bound cryptoproteins yielded an odds ratio ≥2. Eight Ig-bound cryptoproteins were elevated in both pre-diagnostic and newly diagnosed cases compared to controls. Cryptoproteins represent a class of neoantigens that induce an autoantibody response in NSCLC.

A Blood-Based Metabolite Panel for Distinguishing Ovarian Cancer from Benign Pelvic Masses.

PURPOSE: To assess the contributions of circulating metabolites for improving upon the performance of the risk of ovarian malignancy algorithm (ROMA) for risk prediction of ovarian cancer among women with ovarian cysts. EXPERIMENTAL DESIGN: Metabolomic profiling was performed on an initial set of sera from 101 serous and nonserous ovarian cancer cases and 134 individuals with benign pelvic masses (BPM). Using a deep learning model, a panel consisting of seven cancer-related metabolites [diacetylspermine, diacetylspermidine, N-(3-acetamidopropyl)pyrrolidin-2-one, N-acetylneuraminate, N-acetyl-mannosamine, N-acetyl-lactosamine, and hydroxyisobutyric acid] was developed for distinguishing early-stage ovarian cancer from BPM. The performance of the metabolite panel was evaluated in an independent set of sera from 118 ovarian cancer cases and 56 subjects with BPM. The contributions of the panel for improving upon the performance of ROMA were further assessed. RESULTS: A 7-marker metabolite panel (7MetP) developed in the training set yielded an AUC of 0.86 [95% confidence interval (CI): 0.76-0.95] for early-stage ovarian cancer in the independent test set. The 7MetP+ROMA model had an AUC of 0.93 (95% CI: 0.84-0.98) for early-stage ovarian cancer in the test set, which was improved compared with ROMA alone [0.91 (95% CI: 0.84-0.98); likelihood ratio test P: 0.03]. In the entire specimen set, the combined 7MetP+ROMA model yielded a higher positive predictive value (0.68 vs. 0.52; one-sided P < 0.001) with improved specificity (0.89 vs. 0.78; one-sided P < 0.001) for early-stage ovarian cancer compared with ROMA alone. CONCLUSIONS: A blood-based metabolite panel was developed that demonstrates independent predictive ability and complements ROMA for distinguishing early-stage ovarian cancer from benign disease to better inform clinical decision making.

Application of Artificial Intelligence to Plasma Metabolomics Profiles to Predict Response to Neoadjuvant Chemotherapy in Triple-Negative Breast Cancer.

There is a need to identify biomarkers predictive of response to neoadjuvant chemotherapy (NACT) in triple-negative breast cancer (TNBC). We previously obtained evidence that a polyamine signature in the blood is associated with TNBC development and progression. In this study, we evaluated whether plasma polyamines and other metabolites may identify TNBC patients who are less likely to respond to NACT. Pre-treatment plasma levels of acetylated polyamines were elevated in TNBC patients that had moderate to extensive tumor burden (RCB-II/III) following NACT compared to those that achieved a complete pathological response (pCR/RCB-0) or had minimal residual disease (RCB-I). We further applied artificial intelligence to comprehensive metabolic profiles to identify additional metabolites associated with treatment response. Using a deep learning model (DLM), a metabolite panel consisting of two polyamines as well as nine additional metabolites was developed for improved prediction of RCB-II/III. The DLM has potential clinical value for identifying TNBC patients who are unlikely to respond to NACT and who may benefit from other treatment modalities.

Factors modulating 99m Tc-MAA planar lung dosimetry for 90 Y radioembolization.

PURPOSE: To investigate the accuracy and biases of predicted lung shunt fraction (LSF) and lung dose (LD) calculations via 99m Tc-macro-aggregated albumin (99m Tc-MAA) planar imaging for treatment planning of 90 Y-microsphere radioembolization. METHODS AND MATERIALS: LSFs in 52 planning and LDs in 44 treatment procedures were retrospectively calculated, in consecutive radioembolization patients over a 2 year interval, using 99m Tc-MAA planar and SPECT/CT imaging. For each procedure, multiple planar LSFs and LDs were calculated using different: (1) contours, (2) views, (3) liver 99m Tc-MAA shine-through compensations, and (4) lung mass estimations. The accuracy of each planar-based LSF and LD methodology was determined by calculating the median (range) absolute difference from SPECT/CT-based LSF and LD values, which have been demonstrated in phantom and patient studies to more accurately and reliably quantify the true LSF and LD values. RESULTS: Standard-of-care LSF using geometric mean of lung and liver contours had median (range) absolute over-estimation of 4.4 percentage points (pp) (0.9 to 11.9 pp) from SPECT/CT LSF. Using anterior views only decreased LSF errors (2.4 pp median, -1.1 to +5.7 pp range). Planar LD over-estimations decreased when using single-view versus geometric-mean LSF (1.3 vs. 2.6 Gy median and 7.2 vs. 18.5 Gy maximum using 1000 g lung mass) but increased when using patient-specific versus standard-man lung mass (2.4 vs. 1.3 Gy median and 11.8 vs. 7.2 Gy maximum using single-view LSF). CONCLUSIONS: Calculating planar LSF from lung and liver contours of a single view and planar LD using that same LSF and 1000 g lung mass was found to improve accuracy and minimize bias in planar lung dosimetry.

Genotype-to-Phenotype Associations in the Aggressive Variant Prostate Cancer Molecular Profile (AVPC-m) Components.

The aggressive variant prostate cancer molecular profile (AVPC-m), composed of combined defects in TP53, RB1 and PTEN, characterizes a subset of prostate cancers linked to androgen indifference and platinum sensitivity. To contribute to the optimization of the AVPC-m assessment for inclusion in prospective clinical trials, we investigated the status of the AVPC-m components in 28 patient tumor-derived xenografts (PDXs) developed at MDACC. We subjected single formalin-fixed, paraffin-embedded (FFPE) blocks from each PDX to immunohistochemistry (IHC), targeted next-generation genomic sequencing (NGS) and Clariom-S Affymetrix human microarray expression profiling. Standard validated IHC assays and a 10% labeling index cutoff resulted in high reproducibility across three separate laboratories and three independent readers for all tumor suppressors, as well as strong correlations with loss-of-function transcriptional scores (LOF-TS). Adding intensity assessment to labeling indices strengthened the association between IHC results and LOF-TS for TP53 and RB1, but not for PTEN. For TP53, genomic alterations determined by NGS had slightly higher agreement scores with LOF-TS than aberrant IHC, while for RB1 and PTEN, NGS and IHC determinations resulted in similar agreement scores with LOF-TS. Nonetheless, our results indicate that the AVPC-m components can be assessed reproducibly by IHC using various widely available standardized assays.

Natural History of Sudan ebolavirus to Support Medical Countermeasure Development.

Sudan ebolavirus (SUDV) is one of four members of the Ebolavirus genus known to cause Ebola Virus Disease (EVD) in humans, which is characterized by hemorrhagic fever and a high case fatality rate. While licensed therapeutics and vaccines are available in limited number to treat infections of Zaire ebolavirus, there are currently no effective licensed vaccines or therapeutics for SUDV. A well-characterized animal model of this disease is needed for the further development and testing of vaccines and therapeutics. In this study, twelve cynomolgus macaques (Macaca fascicularis) were challenged intramuscularly with 1000 PFUs of SUDV and were followed under continuous telemetric surveillance. Clinical observations, body weights, temperature, viremia, hematology, clinical chemistry, and coagulation were analyzed at timepoints throughout the study. Death from SUDV disease occurred between five and ten days after challenge at the point that each animal met the criteria for euthanasia. All animals were observed to exhibit clinical signs and lesions similar to those observed in human cases which included: viremia, fever, dehydration, reduced physical activity, macular skin rash, systemic inflammation, coagulopathy, lymphoid depletion, renal tubular necrosis, hepatocellular degeneration and necrosis. The results from this study will facilitate the future preclinical development and evaluation of vaccines and therapeutics for SUDV.

Sample Selection Bias in Evaluation of Prediction Performance of Causal Models.

Causal models are notoriously difficult to validate because they make untestable assumptions regarding confounding. New scientific experiments offer the possibility of evaluating causal models using prediction performance. Prediction performance measures are typically robust to violations in causal assumptions. However prediction performance does depend on the selection of training and test sets. In particular biased training sets can lead to optimistic assessments of model performance. In this work, we revisit the prediction performance of several recently proposed causal models tested on a genetic perturbation data set of Kemmeren [5]. We find that sample selection bias is likely a key driver of model performance. We propose using a less-biased evaluation set for assessing prediction performance and compare models on this new set. In this setting, the causal models have similar or worse performance compared to standard association based estimators such as Lasso. Finally we compare the performance of causal estimators in simulation studies which reproduce the Kemmeren structure of genetic knockout experiments but without any sample selection bias. These results provide an improved understanding of the performance of several causal models and offer guidance on how future studies should use Kemmeren.

A first-in-human Phase I trial of the oral p-STAT3 inhibitor WP1066 in patients with recurrent malignant glioma.

Aim: To ascertain the maximum tolerated dose (MTD)/maximum feasible dose (MFD) of WP1066 and p-STAT3 target engagement within recurrent glioblastoma (GBM) patients. Patients & methods: In a first-in-human open-label, single-center, single-arm 3 + 3 design Phase I clinical trial, eight patients were treated with WP1066 until disease progression or unacceptable toxicities. Results: In the absence of significant toxicity, the MFD was identified to be 8 mg/kg. The most common adverse event was grade 1 nausea and diarrhea in 50% of patients. No treatment-related deaths occurred; 6 of 8 patients died from disease progression and one was lost to follow-up. Of 8 patients with radiographic follow-up, all had progressive disease. The longest response duration exceeded 3.25 months. The median progression-free survival (PFS) time was 2.3 months (95% CI: 1.7 months-NA months), and 6-month PFS (PFS6) rate was 0%. The median overall survival (OS) rate was 25 months (95% CI: 22.5 months-NA months), with an estimated 1-year OS rate of 100%. Pharmacokinetic (PK) data demonstrated that at 8 mg/kg, the T1/2 was 2-3 h with a dose dependent increase in the Cmax. Immune monitoring of the peripheral blood demonstrated that there was p-STAT3 suppression starting at a dose of 1 mg/kg. Conclusion: Immune analyses indicated that WP1066 inhibited systemic immune p-STAT3. WP1066 had an MFD identified at 8 mg/kg which is the target allometric dose based on prior preclinical modeling in combination with radiation therapy and a Phase II study is being planned for newly diagnosed MGMT promoter unmethylated glioblastoma patients.